Heat flow control method and heat flow control system

Abstract

A heat flow control method, for a data center cooling system, includes determining a plurality of features corresponding to a current scene of the data center cooling system at a first time point; and determining a plurality of cooling parameters at a second time point according to the plurality of features; wherein the data center cooling system utilizes the plurality of cooling parameters to control heat flow at the second time point; wherein the second time point lags the first time point.

What is claimed is: 
     
         1 . A heat flow control method, for a data center cooling system, the heat flow control method comprising:
 (a) determining a plurality of features corresponding to a current scene of the data center cooling system at a first time point; and   (b) determining a plurality of cooling parameters at a second time point according to the plurality of features;   wherein the data center cooling system utilizes the plurality of cooling parameters to control heat flow at the second time point;   wherein the second time point lags the first time point.   
     
     
         2 . The heat flow control method of  claim 1 , wherein the plurality of features comprise a cold air temperature, a cold air velocity, a server inlet temperature, a server outlet temperature, a server load power, a plurality of primary component temperatures, a server fan speed or a server amount. 
     
     
         3 . The heat flow control method of  claim 1 , wherein the step (b) further comprises:
 utilizing a deep learning method to perform a decision-fuse procedure for the plurality of features to generate the plurality of cooling parameters.   
     
     
         4 . The heat flow control method of  claim 3 , wherein the deep learning method adopts at least one of a deep neural network (DNN), a deep belief network (DBN), a convolutional neural network (CNN) and a convolutional deep belief network (CDBN). 
     
     
         5 . The heat flow control method of  claim 1 , wherein the plurality of cooling parameters comprise a predicted server inlet temperature and a predicted server fan speed. 
     
     
         6 . A heat flow control system, for a data center cooling system, the heat flow control system comprising:
 a processor; and   a memory, coupled to the processor, stores a programing code to indicate the processor to perform a transmission parameter decision method, wherein the transmission parameter decision method comprises:
 (a) determining a plurality of features corresponding to a current scene of the data center cooling system at a first time point; and 
 (b) determining a plurality of cooling parameters at a second time point according to the plurality of features; 
 wherein the data center cooling system utilizes the plurality of cooling parameters to control heat flow at the second time point; 
 wherein the second time point lags the first time point. 
   
     
     
         7 . The heat flow control system of  claim 6 , wherein the plurality of features comprise a cold air temperature, a cold air velocity, a server inlet temperature, a server outlet temperature, a server load power, a plurality of primary component temperatures, a server fan speed or a server amount. 
     
     
         8 . The heat flow control system of  claim 6 , wherein the step (b) further comprises:
 utilizing a deep learning method to perform a decision-fuse procedure for the plurality of features to generate the plurality of cooling parameters.   
     
     
         9 . The heat flow control system of  claim 8 , wherein the deep learning method adopts at least one of a deep neural network (DNN), a deep belief network (DBN), a convolutional neural network (CNN) and a convolutional deep belief network (CDBN). 
     
     
         10 . The heat flow control system of  claim 6 , wherein the plurality of cooling parameters comprise a predicted server inlet temperature and a predicted server fan speed.